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HybrInt – Hybrid Intelligence through Interpretable AI

The aim of this project is to strengthen basic AI research jointly at Leibniz University Hannover and Osnabrück University in the name of hybrid intelligence. The key idea is to combine the strengths of the complementary heterogeneous intelligence of humans and machine: human intelligence is defined by the ability to learn, reason, and interact with the environment based on their knowledge, whereas AI is attributed to machines. This includes tasks, such as language processing, object recognition, model building, and applying that knowledge to solve problems.

With the overarching goal, the research is structured by seven subprojects (SP) ranging from basic research to application and hardware. These project subsume research on knowledge graphs, explainable AI, resource-efficient hardware acceleration, reinforcement learning, trustworthiness, robotics, and human-centered explanations.

• SP1: Knowledge Graph-based Extraction of Research Knowledge from Articles;
• SP2: Knowledge-Graph-Based Reinforcement Learning;
• SP3: Knowledge Graph-based Interpretable Learning on Complex Data;
• SP4: Algorithm Hardware Codesign for Resource-Efficient Interpretable AI Methods;
• SP5: Robust Online Single Plant Classification from Multimodal Sensor Data Including Semantic Context Knowledge;
• SP6: Credible and Structured Interpretations of Machine Learning Models;
• SP7: Human-Centered Explanation of Machine Learning Results.

The envisaged research cooperation should manifest itself in two joint use cases in the field of high precision farming.

1. Optimizing Irrigation – Agricultural Water Management. We aim to develop novel methods, where expert knowledge on irrigation optimization can be incorporated in a human-understandable fashion and new knowledge can be extracted from the learning agent’s experience to enrich human expertise.
2. Agricultural Research Knowledge Observatory. We aim to retrieve relevant literature addressing biodiversity, agricultural, and plant-related knowledge questions, and to create structured contribution descriptions for each of the found articles. In addition, we also aim to link the literature to relevant datasets and possibly other artifacts such as images, videos etc.

HybrInt Retreat - September 13, 2024

To strengthen the cooperation among sub-projects, enhance the work, and discussion within the project as a whole, the Retreat of the HybrInt project has been held at Coppenrath Innovation Centre (CIC) Osnabrück and Gut Arenshorst Bohmte on September 12th and 13th, 2024.

Publications

SP1:

1. Zhiyin Tan, Jennifer D’Souza
   "Bridging the Evaluation Gap: Leveraging Large Language Models for Topic Model Evaluation"
   IRCDL'25: 21st conference on Information and Research science Connecting to Digital and Library science, Feb 20-21, 2025, Udine, Italy.
   Paper: https://ceur-ws.org/Vol-3937/paper15.pdf
   Github: https://github.com/zhiyintan/topic-model-LLMjudgment

SP2:

1. Maximilian Schier, Frederik Schubert, Bodo Rosenhahn
   "Explainable Reinforcement Learning via Dynamic Mixture Policies"
   2025 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
   Paper : https://www.tnt.uni-hannover.de/papers/data/1769/ICRA_2025-4.pdf
   Github : https://github.com/m-schier/Explainable-RL-Dynamic-Mixture-Policies

2. Yannik Mahlau, Frederik Schubert, Bodo Rosenhahn
   "Mastering Zero-Shot Interactions in Cooperative and Competitive Simultaneous Games"
   Proceedings of the 41st International Conference on Machine Learning (ICML), July 2024.
   Paper: https://openreview.net/forum?id=SoqxSnEUi1
   Github:https://github.com/ymahlau/albatross

SP3:

1. Thomas Norrenbrock, Timo Kaiser, Sovan Biswas, Ramesh Manuvinakurike, Bodo Rosenhahn
   "QPM: Discrete Optimization for Globally Interpretable Image Classification"
   The Thirteenth International Conference on Learning Representations (ICLR), April 2025. 
   Paper: openreview.net/forum
   Github: https://github.com/ThomasNorr/QPM

2. Thomas Norrenbrock, Marco Rudolph, Bodo Rosenhahn
   "Q-SENN: Quantized Self-Explaining Neural Networks"
   AAAI Technical Track on Safe, Robust and Responsible AI, AAAI Press, Vol. 38, No. 19, pp. 21482-21491, Vancouver, Canada, February 2024, edited by Michael J. Wooldridge and Jennifer G. Dy and Sriraam Natarajan.
   Paper: https://dl.acm.org/doi/10.1609/aaai.v38i19.30145
   Github: https://github.com/ThomasNorr/Q-SENN

3. Dan Hudson, Jurgen Van Den Hoogen, Stefan Bloemheuvel, Martin Atzmueller
   "Stay tuned! Analysing hyperparameters of a wide-kernel architecture for industrial faults" 
   2024 IEEE Conference on Artificial Intelligence (CAI).
   Paper: https://ieeexplore.ieee.org/document/10605520

4. Jurgen van den Hoogen, Dan Hudson, Martin Atzmueller
   "Graph Signal Processing Unearths the Best Locations for Soil Moisture Sensors"
   2024 International Conference on Machine Learning and Applications (ICMLA).
   Paper: ieeexplore.ieee.org/abstract/document/10903245

SP4:

1. Marc Rothmann, Mario Porrmann
   "FPGA-based Acceleration of Deep Q-Networks with STANN-RL"
   9th IEEE International Conference on Fog and Mobile Edge Computing, FMEC 2024.
   Paper: doi.org/10.1109/FMEC62297.2024.10710277

2. Yufei Mao, Rolands Weiss, Yi Zhang, Yu Li, Marc Rothmann, Mario Porrmann
   "FPGA Acceleration of DL-Based Real-Time DC Series Arc Fault Detection"
   31st Reconfigurable Architectures Workshop, RAW 2024, San Francisco, CA, USA, May 27th-28th 2024, pp.92-98.
   Paper: doi.org/10.1109/IPDPSW63119.2024.00031

3. Patrick Glandorf, Rosenhahn Bodo
   “Pruning by Block Benefit: Exploring the Properties of Vision Transformer Blocks during Domain Adaptation”
   International Conference on Computer Vision Workshops (ICCVW), October 2025
   Paper: https://arxiv.org/abs/2506.23675.pdf

SP5:

1. Mariia Khan, Yue Qiu, Yuren Cong, Bodo Rosenhahn, David Suter, Jumana Abu-Khalaf
   "Indoor Scene Change Understanding (SCU): Segment, Describe, and Revert Any Change"
   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , IEEE, Abu Dhabi, United Arab Emirates , October 2024.
   Paper : https://ieeexplore.ieee.org/document/10801354
   Github: https://github.com/mariiak2021/EmbSCU

2. Mathis Kruse, Marco Rudolph, Dominik Woiwode, Bodo Rosenhahn
   "SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection"
   {IEEE/CVF} Conference on Computer Vision and Pattern Recognition, {CVPR} 2024 - Workshops, IEEE, pp. 3950-3960, June 2024.
   Paper: https://arxiv.org/abs/2404.06832
   Github: https://github.com/m-kruse98/SplatPose

3. Mathis Kruse, Bodo Rosenhahn
   “Multi-Flow: Multi-View-Enriched Normalizing Flows for Industrial Anomaly Detection”
   Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops, pp. 3933--3944, June 2025.
   Paper: www.tnt.uni-hannover.de/papers/data/1782/multiflow_arxiv.pdf 
   Github: github.com/m-kruse98/Multi-Flow

4. Tom Wehrbein, Marco Rudolph, Bodo Rosenhahn, Bastian Wandt
   “Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery”
   IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), IEEE, February 2025.
   Paper: www.tnt.uni-hannover.de/papers/data/1751/1735_arxiv_version.pdf 
   Github: github.com/twehrbein/humr 

5. Melanie Schaller, Mathis Kruse, Antonio Ortega, Marius Lindauer, Bodo Rosenhahn 
   “AutoML for Multi-Class Anomaly Compensation of Sensor Drift” 
   Measurement, 2025. 
   Paper: arxiv.org/pdf/2502.19180 
   Github: github.com/MilanShao/AutoML-for-Multi-Class-Anomaly-Compensation-of-Sensor-Drift

6. Timo Kaiser, Maximilian Schier, Bodo Rosenhahn
   “Cell Tracking according to Biological Needs - Strong Mitosis-aware Multi-Hypothesis Tracker with Aleatoric Uncertainty”
   Transactions on Medical Imaging, IEEE, 2025.
   Paper: https://ieeexplore.ieee.org/document/11051031
   Github: github.com/TimoK93/BiologicalNeeds

SP6:

1. Alexander Dockhorn, Rudolf Kruse
   "An overview of Reinforcement Learning Algorithms for Causaul discovery"
   Handbook of Artificial Intelligence and Machine Learning in Decision Making. Springer Nature.

2. Seham Nasr, Sandipan Sikdar
   "IndMask: Inductive Explanation for Multivariate Time Series Black-Box Models"
   ECAI 2024, Vol. 392.
   Paper: doi.org/10.3233/FAIA240603

3. Jonas Wallat, Hauke Hinrichs, Avishek Anand
   "Causal Probing for Dual Encoders"
   CIKM’24: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp.2292-2303.
   Paper: https://dl.acm.org/doi/abs/10.1145/3627673.3679556

4. Jonas Wallat, Maria Heuss, Maarten de Rijke, Avishek Anand
   “Correctness Is Not Faithfulness in Retrieval-Augmented  Generation Attributions”
   Proceedings of the 2025 International ACM SIGIR Conference on Innovative Concepts and Theories in Information Retrieval (ICTIR), 22-32
   Paper: dl.acm.org/doi/pdf/10.1145/3731120.3744592
   Github: github.com/jwallat/RAG-attributions

5. Jonas Wallat, Abdelrahman Abdallah, Adam Jatowt, Avishek Anand
   “A Study into Investigating Temporal Robustness of LLMs”
   Findings of ACL 2025, 15685–15705
   Paper: arxiv.org/abs/2503.17073 
   Github: github.com/jwallat/temporalrobustness

6. Aparup Khatua, Tobias Kalmbach, Prasenjit Mitra, Sandipan Sikdar
   “Evaluating LLMs' (In)ability to Follow Prompts in QA Tasks”
   Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2941-2945
   Paper: dl.acm.org/doi/abs/10.1145/3726302.3730190
   Github: github.com/Sandipan99/Oedipus

7. Leon Wichert, Sandipan Sikdar
   “Rethinking Evaluation Methods for Machine Unlearning”
   Findings of EMNLP 2024, 4727–4739
   Paper: aclanthology.org/2024.findings-emnlp.271.pdf
   Github: github.com/Kartoffelpuffa/Rethinking-MU-Evaluation  

SP7:

1. Timo Kaiser, Thomas Norrenbrock, Bodo Rosenhahn
   “UncertainSAM: Fast and Efficient Uncertainty Quantification of the Segment Anything Model”
   Forty-second International Conference on Machine Learning (ICML), PLMR, Vancouver, July 2025
   Paper: www.tnt.uni-hannover.de/papers/data/1784/USAM.pdf
   Github: github.com/GreenAutoML4FAS/UncertainSAM

2. Leandra Fichtel, Maximilian Spliethöver, Eyke  Hüllermeier, Patricia Jimenez, Nils Klowait, Stefan Kopp, Axel-Cyrille Ngonga Ngomo, Amelie Robrecht, Ingrid Scharlau, Lutz Terfloth, Anna-Lisa Vollmer and Henning Wachsmuth
   “Investigating Co-Constructive Behavior of Large Language Models in Explanation Dialogues”
   Conference or journal name: Special Interest Group on Discourse and  Dialogue (SIGDIAL)
   Paper: arxiv.org/pdf/2504.18483
   Github: github.com/webis-de/SIGDIAL-25

3. Yuren Cong, Martin Renqiang Min, Li Erran, Bodo Rosenhahn, Michael Ying Yang
   “Attribute-Centric Compositional Text-to-Image Generation”
   International Journal of Computer Vision, Springer, Vol. 133, p. 4555–4570, March 2025
   Paper: link.springer.com/article/10.1007/s11263-025-02371-0
   Github: github.com/yrcong/ACTIG